Mikayla Joslin
Professor Yungfei Kao
PSYCH 3553: Psychological Statistics
January 19, 2022
Chapter 1
1. Population: In a particular study, a population is a set of all the individuals of interest.
“As you can well imagine, a population can be quite large – for example, the entire set of
women on the planet Earth” (Gravetter & Wallnau, p.3, 2017).
2. Sample: Like a population, a sample is a set of individuals selected from a particular
population, and it is usually intended to represent that particular population in a research
study. “One study might examine a sample of only 10 students in a graduate program and
another study might use a sample of more than 10,000 people who take a specific
cholesterol medication” (p.4).
3. Parameter: A parameter is usually derived from measurements, usually a numerical
value that describes a population, and it is measured from the individuals in the
population.
4. Statistic: Summarizing, organizing, and interpreting information refers to a set of
mathematical procedures, known as, a statistic. A statistic is known as a value, usually a
numerical value, that describes a particular sample. A statistic comes from measurements
of the individuals in this sample. “Statistics consists of facts and figures such as the
average annual snowfall in Denver or Derrick Jeter’s lifetime batting average” (p.2).
5. Variable: A variable is a characteristic or condition that is liable to change or be adapted
and. have different values for different individuals.
6. Data: Data is known to be measurements, observations, facts, and statistics used to
collect analysis.
7. Descriptive Statistics: Like a statistic, a descriptive statistic is a part of statistical
procedures used to summarize, organize, and simplify data. “Descriptive statistics,
consists of statistical procedures that are used to simplify and summarize data” (p.5).
8. Inferential Statistics: Inferential statistics consist of rules that allow us to study samples
and then make observations about the populations from which they were selected.
“Inferential statistics are methods that use sample data to make general statements about
a population” (p.6).
9. Correlational Method: In the correlation method, two fluctuating variables are observed
to determine whether there is a relationship between the two. “When the data from a
correlation study consist of numerical scores, the relationship between the two variables
is usually measured and described using a statistic called a correlation” (p.11).
10. Experimental Method: In the experimental method one variable is changed while
another variable is left alone to be observed and measured. To put in place a cause-andeffect relationship between the two variables, an experiment wants to control all other
variables to prevent them from changing the results.
11. Independent Variable: An independent variable is the variable that is controlled by the
researcher. The independent variable usually consists of the two, and sometimes more,
treatment conditions to which subjects are disclosed in a behavioral study. The
independent variable consists of the previous conditions that were changed prior to
observing the dependent variable.
12. Dependent Variable: A dependent variable is the variable that is left alone, as is, and is
observed to assess the effect of the treatment
13. Control Condition: Individuals in a control condition do not receive any type of
experimental treatment. Instead, they either receive no treatment or they receive a
placebo effect treatment. The purpose of control condition is to provide a basic
observation for comparing the experimental condition.
14. Experimental Condition: Unlike individuals in the control condition, individuals in the
experimental condition do receive an experimental treatment and not a placebo.
15. Operational Definition: Identifies a set of operations for measuring an outside behavior
and uses the resulting data as a definition and a measurement of a theoretical construct.
An operational definition has two components, first, it describes a set of examinations for
measuring a construct. Second, it defines the construct in terms of the resulting data.
16. Scale of Measurement: refers to ways in which variables and/or numbers are understood
and placed into categories. Each scale of measurement has distinct properties which in
determines the suitability for use of certain data analyses. The four scales of measurement
are nominal, ordinal, interval, and ratio. “The categories used to measure a variable make
up a scale of measurement, and the relationship between the categories determine
different types of scales. The distinction among the scales are important because they
identify the limitations of certain types of measurements and because certain statistical
procedures are appropriate for scores that have been measured on some scales but not on
others” (p,21).
17. Nominal Scale: Consists of a set of classifications that have different names.
Measurements on a nominal scale label and categorize observations, but do not make any
computable distinctions between observations. “The word nominal means “having to do
with names.” Measurements on a nominal scale involves classifying individuals into
categories that have different names but are not related to each other in any systematic
way. For example, if you were measuring the academic majors for a group of college
students, the categories would be art, biology, business, chemistry, and so on” (p,21).
18. Ordinal Scale: Consists of a set of categories that are organized in an ordered
arrangement. Size or magnitude are the scale rank measurements on an ordinal scale.
“Often, an ordinal scale consists of a series of ranks (first, second, third, and so on) like
the order of finish in a horse race. Occasionally, the categories are identified by verbal
labels like small, medium, and large drink sizes at a fast-food restaurant” (p,22).
19. Interval Scale: Interval scale consists of arranged categories that are all exactly the same
size. Equal differences between numbers on scale reflect equal differences in magnitude.
However, the zero point on an interval scale is random and does not indicate a zero
amount of the variable being measured. “An interval scale has an arbitrary zero point.
That is, the value 0 is assigned to a particular location on the scale simply as a matter of
convenience or reference” (p,22).
20. Ratio Scale: is an interval scale with the additional feature of an absolute zero point.
With a ratio scale, ratios of numbers do reflect ratios of magnitude. “A ratio scale is
anchored by a zero point that is arbitrary but rather is meaningful value representing none
(a complete absence) of the variable being measured. The existence of an absolute, nonarbitrary zero point means that we can measure the absolute amount of the variable; that
is, we can measure the distance from 0” (p.23).